Gabriel Angelo Coñejos
Papers
1
Total Citations
1
H-Index
1
About
Gabriel Angelo Coñejos is a researcher at the forefront of agricultural automation and intelligent post-harvest systems. His primary research areas include robotic manipulation, computer vision, and transfer learning for quality assessment in fresh produce. Coñejos’s most notable contribution is his work on an automated bell pepper quality assessment system, which integrates a robotic gripper with deep learning to address the inefficiencies of manual sorting—a process that is often time-consuming and inconsistent. By applying transfer learning, his system enhances the accuracy and speed of grading, directly impacting marketability by aligning with consumer standards. His 2025 paper, "Automated Bell Pepper Quality Assessment: Robotic Gripper Sorting System with Transfer Learning," has already garnered early citations, signaling its relevance to the agricultural robotics community. Coñejos’s work bridges the gap between engineering and agronomy, offering scalable solutions for post-harvest handling. His achievements highlight a commitment to reducing labor dependency and improving food quality assurance, making him a promising voice in the field of smart agriculture and automated food processing.
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Top Papers
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